Data Analytics & Visualization

level of course unit

second cycle, Master

Learning outcomes of course unit

The graduate / student:* can describe the contents, results/applications and working methods of Data Science
* can convert "questions" into requirements in the context of Data Science
* can define the process and tools based on these and implement / use them
* knows a software with libraries for implementing data analysis and evaluation
* can use appropriate software
* can carry out suitable evaluations and analyses using the software for defined examples

prerequisites and co-requisites

none

course contents

* Introduction (data, information, knowledge, temporal components, objectives)
* Data process (collection, preparation, analysis, presentation)
* Data preparation (cleansing, transformation, rescaling, storage)
* Approaches for the analysis of data
* Presentation/visualization of results
* Software (open source and proprietary software)
* Machine Learning - process, approaches, implementation
* Introduction to the software used e.g. Python
* Collecting and preparing data using software
* Analysis and presentation of sample data using various approaches (e.g. regression, decision trees, etc.)